c9w1l2 rCharts

rCharts Basics

rCharts is a package that uses javascript to create interactive visualizations for R. It (kind of) emulates the javascript D3 library.

For more info, see

• http://ramnathv.github.io/rCharts/
• https://github.com/ramnathv/rCharts

Install rCharts with the following:

require(devtools)
install_github(repo = 'rCharts', username = 'ramnathv')

Much of the interactivity is baked in by default via the various d3js libraries called by rCharts.

*Note: rChart plots render differently in knitr vs console. See StackOverflow.

For knitr:

• plot with myPlot$print(title = 'title', include_assets=TRUE)
• r chunk options must include results='asis'
• print the raw html with option results='markup'

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For console:

• plot by calling the variable: myPlot;
• or by calling the plotting function: rPlot(...);

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For RPubs:

• Use knitr with listed options
• use myPlot$print(title = 'title', include_assets=TRUE, cdn=TRUE)
  • cdn=TRUE instructs document to serve JS+CSS from RPubs rather than your local directory asset library

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Example 1: MorrisJS Plotting Library

In this example, reshape2::transform() is loaded automatically by rCharts.

require(rCharts)

data(economics, package = "ggplot2")
econ <- transform(economics, date = as.character(date))
m1 <- mPlot(x = "date", y = c("psavert", "uempmed"), type = "Line", data = econ)
m1$set(pointSize = 0, lineWidth = 1)
m1$print("chart2", include_assets=TRUE)

## Loading required package: reshape2

Per http://ramnathv.github.io/rCharts/, rCharts has the following default libraries, invoked by the corresponding rChart functions.
*Note: It appears that only Polychart and MorrisJS work out-of-the-box with rChart. The remainder need some additional libraries or configuration.

• Polychart: rPlot()
  • Grammer of Graphics syntax style, similar to ggPlot2
  • https://github.com/Polychart/polychart2
  • 

require(rCharts)

r1 <- rPlot(mpg ~ wt | am + vs, data = mtcars, type = "point", color = "gear")
r1$print("Polychart", include_assets=TRUE)

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• MorrisJS: mPlot()
  • time series graphs: line, bar, area, donut style
  • http://morrisjs.github.io/morris.js/
  • https://github.com/oesmith/morris.js
  • 

require(rCharts)

data(economics, package = "ggplot2")
econ <- transform(economics, date = as.character(date))
m2 <- mPlot(x = "date", y = c("psavert", "uempmed"), type = "Line", data = econ)
m2$set(pointSize = 0, lineWidth = 1)
m2$print("Morris", include_assets=TRUE)

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• NVD3: nPlot()
  • charts that require little customization, based reusable chart components
  • http://nvd3.org/
  • https://github.com/novus/nvd3
  • http://bost.ocks.org/mike/chart/
  • 

require(rCharts)

hair_eye_male <- subset(as.data.frame(HairEyeColor), Sex == "Male")
n1 <- nPlot(Freq ~ Hair, group = "Eye", data = hair_eye_male, type = "multiBarChart")
n1$print("NVD3", include_assets=TRUE, cdn=FALSE)

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• xCharts: xPlot()
  • includes SVG for infinitely scalable charts
  • http://tenxer.github.io/xcharts/
  • https://github.com/tenXer/xcharts/
  • 

require(rCharts)
require(reshape2)

uspexp <- melt(USPersonalExpenditure)
names(uspexp)[1:2] = c("category", "year")
x1 <- xPlot(value ~ year, group = "category", data = uspexp, type = "line-dotted")
x1$print("xCharts", include_assets=TRUE)

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• HighCharts: hPlot()
  • claims to be simples & most flexible, free for non-commercial
  • related to js libraries HighStock (time series), HighMaps (maps), HighChartsCloud (cloud deployed charts)
  • http://www.highcharts.com/
  • Features: http://www.highcharts.com/products/highcharts/#open
  • 

require(rCharts)

h1 <- Highcharts$new()
h1$chart(type = "spline")
h1$series(data = c(1, 3, 2, 4, 5, 4, 6, 2, 3, 5, NA), dashStyle = "longdash")
h1$series(data = c(NA, 4, 1, 3, 4, 2, 9, 1, 2, 3, 4), dashStyle = "shortdot")
h1$legend(symbolWidth = 80)

h1$print("HighCharts", include_assets=TRUE)

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• Leaflet: Leaflet$new()
  • interactive maps
  • http://leafletjs.com/
  • Features: http://leafletjs.com/#features
  • 36 Plugins: http://leafletjs.com/plugins.html
  • 

require(rCharts)

map3 <- Leaflet$new()
map3$setView(c(51.505, -0.09), zoom = 13)
map3$marker(c(51.5, -0.09), bindPopup = "<p> Hi. I am a popup </p>")
map3$marker(c(51.495, -0.083), bindPopup = "<p> Hi. I am another popup </p>")
map3$print("Leaflet", include_assets=TRUE)

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• Rickshaw: Rickshaw$new()
  • time series charts
  • https://github.com/shutterstock/rickshaw
  • 

require(rCharts)

usp = reshape2::melt(USPersonalExpenditure)
# get the decades into a date Rickshaw likes
usp$Var2 <- as.numeric(as.POSIXct(paste0(usp$Var2, "-01-01")))
p4 <- Rickshaw$new()
p4$layer(value ~ Var2, group = "Var1", data = usp, type = "area", width = 560)
# add a helpful slider this easily; other features TRUE as a default
p4$set(slider = TRUE)
p4$print("Rickshaw", include_assets=TRUE, cdn=TRUE)

Publishing rCharts

Publishing in any format is done in 2 steps

1. Save the rCharts plot to a variable
   • r1 <- rPlot(...)
2. Do a framework-specific publishing step
   • console, browser, knitr, RPubs, Gist, Slidify, Shiny

In general

• r1$html()
  • prints the html + js + css for the plot
• r1$save("filename.html", cdn=FALSE)
  • saves plot to html file, can be loaded into browser
  • when hosting remotely (~RPubs), cdn=TRUE serves assets from the remote source
• r1$print(title="one_word_title", include_assets={TRUE,FALSE})
  • for knitr, include_assets=TRUE
• `r1$publish(title=‘title’, host={‘gist’, ‘rpubs’, etc.})
  • publishes to externally hosted site

All static publishing is done with an rCharts::_Plot::publish() function.

Dynamic charts are embedded into Shiny by calling
rCharts::renderChart(expr = <all code>) in server.r
and
rCharts::showOutput(outputId='<name of chart var: server::output$_____>', lib='js_plotting_lib') in the mainPanel() function of ui.r.

r1 <- rPlot(...)
r1$publish(title = 'Title', host = {'rpubs', 'gist', etc.})

Console

Simply call the plot object, ex r1;

Knitr

Print the object with r1$print(title='unique_title', include_assets=TRUE) using knitr r chunk option {r results='asis}.
Each chart title in a document must be unique, or knitr will print the charts in the wrong place.

require(rCharts)

names(iris) = gsub("\\.", "", names(iris))

r1 <- rPlot(SepalLength ~ SepalWidth | Species, data = iris, color = 'Species', type = 'point')

r1$print('Unique_Title', include_assets=TRUE)

RPubs

require(rCharts)

names(iris) = gsub("\\.", "", names(iris))

r1 <- rPlot(SepalLength ~ SepalWidth | Species, data = iris, color = 'Species', type = 'point')

r1$publish('Scatterplot', host = 'rpubs')

Gist

require(rCharts)

names(iris) = gsub("\\.", "", names(iris))
r1 <- rPlot(SepalLength ~ SepalWidth | Species, data = iris, color = 'Species', type = 'point')

r1$publish('Scatterplot', host = 'gist')

Slidify Interactive

Embedding in Slidify requires an additional line in the YAML header: yaml ext_widgets : {rCharts: ["libraries/nvd3", "libraries/morris", etc]}

1. Save the plot to file
   • obj$save("file.html")
2. Embed into slidify
   • cat('<iframe src="path/to/file.html" width=100%, height=600></iframe>')

require(rCharts)

names(iris) = gsub("\\.", "", names(iris))
r1 <- rPlot(SepalLength ~ SepalWidth | Species, data = iris, color = 'Species', type = 'point')

cat('<iframe src="r1.html" width=100%, height=600></iframe>')

Shiny

• must return(plot_obj) the plot object inside renderChart()

## server.r
require(rCharts)
shinyServer(function(input, output) {
    
  # rCharts::renderChart() takes all the calculation & plotting code
  # and stores the result in the output$myChart; call showOutput('myChart') in ui.r
  output$myChart <- renderChart({
    
    names(iris) = gsub("\\.", "", names(iris))
    p1 <- rPlot(input$x, input$y, data = iris, color = "Species", 
      facet = "Species", type = 'point')
    p1$addParams(dom = 'myChart')
    return(p1)
  })
})

## ui.R
require(rCharts)
shinyUI(pageWithSidebar(
  headerPanel("rCharts: Interactive Charts from R using polychart.js"),

  sidebarPanel(
    selectInput(inputId = "x",
     label = "Choose X",
     choices = c('SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'),
     selected = "SepalLength"),
    selectInput(inputId = "y",
      label = "Choose Y",
      choices = c('SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'),
      selected = "SepalWidth")
  ),
  mainPanel(
    
    # call by the name of server::output#myChart;
    # 'polycharts' is the JS lib that does the plotting 
    showOutput("myChart", "polycharts")
  )
))

Examples

Facetted Scatterplot

With the Polycharts library, use rPlot(..., type = 'point')

require(rCharts)

names(iris) = gsub("\\.", "", names(iris))
r1 <- rPlot(SepalLength ~ SepalWidth | Species, data = iris, color = 'Species', type = 'point')

r1$print("Facetted_Scatterplot", include_assets=TRUE)

Facetted Barplot

With the Polycharts library, use rPlot(..., type = 'bar')

require(rCharts)

hair_eye = as.data.frame(HairEyeColor)
r2 <-rPlot(Freq ~ Hair | Eye, data = hair_eye, color = 'Eye', type = 'bar')

r2$print("Facetted_Barplot", include_assets=TRUE)

Time Series Line Chart

With MorrisJS, type = 'Line'. Notice also that there are 2 series in this plot via y = c('psavert', 'uempmed'); unique to Morris??

require(rCharts)

data(economics, package = "ggplot2")
econ <- transform(economics, date = as.character(date))
m1 <- mPlot(x = "date", y = c("psavert", "uempmed"), type = "Line", data = econ)
m1$set(pointSize = 0, lineWidth = 1)
m1$print("Time_Series", include_assets=TRUE)